Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "135" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 37 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 35 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459852 | digital_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.088031 | 16.608631 | 5.802777 | 28.351495 | 2.426509 | 20.034128 | 7.883218 | 16.376252 | 0.7816 | 0.0338 | 0.4596 | 8.132465 | 1.203491 |
| 2459851 | digital_maintenance | 100.00% | 0.00% | 98.28% | 0.00% | 100.00% | 0.00% | -1.015484 | 21.081065 | 6.623990 | 30.273206 | 2.374841 | 43.619041 | 1.951506 | 19.248025 | 0.7060 | 0.0694 | 0.4677 | 2.824847 | 1.186150 |
| 2459850 | digital_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.525529 | 18.698659 | 5.587029 | 25.193428 | 0.514666 | 19.971260 | 0.720318 | 14.354501 | 0.6965 | 0.0403 | 0.4598 | 3.148474 | 1.201837 |
| 2459849 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459848 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.872784 | 0.046593 | -0.653302 | -0.367069 | 0.326129 | 0.040656 | 0.214865 | -0.144853 | 0.6819 | 0.7305 | 0.4017 | 3.310738 | 3.067636 |
| 2459847 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.199933 | -0.109145 | -0.494378 | -0.091815 | 0.121114 | 0.124797 | 0.691008 | -0.023156 | 0.6920 | 0.6624 | 0.4476 | 7.777441 | 5.662847 |
| 2459845 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.625575 | -0.120796 | -0.039788 | -0.919268 | 0.293951 | -0.274348 | 2.057112 | 0.329851 | 0.6889 | 0.7159 | 0.3966 | 0.000000 | 0.000000 |
| 2459844 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.089286 | -0.589371 | -0.021252 | 0.756137 | 0.015244 | -0.550210 | 0.755617 | 0.239145 | 0.0265 | 0.0246 | 0.0013 | nan | nan |
| 2459843 | digital_maintenance | 0.00% | 1.20% | 0.66% | 0.00% | 100.00% | 0.00% | -0.970414 | 0.571934 | -0.479900 | 2.909079 | -0.347972 | 2.658965 | 0.672608 | 0.395658 | 0.7090 | 0.7148 | 0.4015 | 3.431947 | 2.981005 |
| 2459842 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.842866 | -0.116295 | -0.245523 | 2.192107 | 0.291778 | 0.174430 | 0.559489 | 0.550305 | 0.7422 | 0.6366 | 0.2827 | 4.308655 | 3.840917 |
| 2459841 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 1.316608 | 3.463715 | -0.780282 | 2.268893 | -0.128023 | -1.312248 | 1.057442 | 0.131130 | 0.0273 | 0.0245 | 0.0019 | nan | nan |
| 2459840 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 123.370365 | 178.189959 | 62.792671 | 95.196935 | 1714.561851 | 10095.822977 | 1903.011361 | 5348.794008 | 0.0196 | 0.0163 | 0.0023 | nan | nan |
| 2459839 | digital_maintenance | 100.00% | - | - | - | - | - | 33.252780 | 57.751576 | 171.482508 | 338.238260 | 293.418959 | 1149.695646 | 2200.176445 | 8038.498113 | nan | nan | nan | nan | nan |
| 2459838 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.227682 | 0.126824 | -1.040233 | -0.763148 | 0.550772 | 1.353649 | -0.012972 | -0.395604 | 0.6546 | 0.6317 | 0.4061 | 0.000000 | 0.000000 |
| 2459836 | digital_maintenance | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0352 | 0.0337 | 0.0013 | nan | nan |
| 2459835 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -1.001498 | -0.239844 | -0.456043 | -1.012562 | -0.634340 | -1.048671 | 0.609285 | -0.470720 | 0.0361 | 0.0333 | 0.0015 | nan | nan |
| 2459833 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.671448 | -1.045131 | 0.512969 | 0.675843 | 0.653487 | 2.413572 | 2.033700 | 0.177135 | 0.0324 | 0.0317 | 0.0018 | nan | nan |
| 2459832 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.274468 | -0.166524 | -1.091725 | -0.631776 | 0.161877 | -0.126737 | 0.689732 | -0.279811 | 0.7395 | 0.4588 | 0.5471 | 3.825100 | 2.996166 |
| 2459831 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -1.098854 | -1.214417 | -0.277623 | 0.477991 | 0.004178 | 1.386554 | 1.727950 | -0.235108 | 0.0351 | 0.0317 | 0.0028 | nan | nan |
| 2459830 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.314623 | -0.010609 | -1.091157 | -0.979768 | -0.504443 | 0.360673 | 1.402941 | 0.059715 | 0.7401 | 0.4788 | 0.5275 | 4.432546 | 3.247637 |
| 2459829 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.327892 | 0.697708 | -0.933558 | -0.901711 | 0.024275 | 0.408488 | 0.947434 | -0.282335 | 0.6728 | 0.5972 | 0.4009 | 8.092853 | 6.742973 |
| 2459828 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.691716 | 0.570654 | -0.697732 | -0.751137 | 0.131768 | -0.648938 | 3.243314 | 1.942140 | 0.7328 | 0.4791 | 0.5078 | 6.402862 | 3.200042 |
| 2459827 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.589469 | 0.156941 | -0.833998 | -0.746646 | -0.339981 | -0.099854 | 1.398413 | 1.459807 | 0.0804 | 0.0910 | 0.0144 | 1.316968 | 1.316333 |
| 2459826 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.187826 | -0.022717 | -0.805722 | -0.833903 | -0.012712 | -0.649264 | 0.582925 | -0.369614 | 0.0901 | 0.0965 | 0.0240 | 4.801142 | 7.110972 |
| 2459825 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.109242 | 0.210012 | -1.023716 | -0.702254 | -0.645211 | -0.524133 | 0.729328 | 0.109290 | 0.0775 | 0.0903 | 0.0196 | 1.200425 | 1.187491 |
| 2459824 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.438987 | -0.474249 | -1.029780 | -0.902210 | 0.140384 | -0.085869 | 1.455356 | -0.117540 | 0.0805 | 0.0879 | 0.0127 | 1.196184 | 1.210900 |
| 2459823 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.505161 | 0.136650 | -0.819738 | -0.795755 | -0.856199 | -1.314302 | -0.027468 | -0.254507 | 0.0800 | 0.0830 | 0.0180 | 1.295809 | 1.293197 |
| 2459822 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.096550 | 0.439456 | -0.934749 | -0.891535 | -0.358732 | -0.946555 | 2.799916 | 4.535869 | 0.0959 | 0.0911 | 0.0262 | 1.275869 | 1.278106 |
| 2459821 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.477998 | 0.278335 | -1.079256 | -0.919261 | -0.512177 | -0.786278 | 0.619999 | 0.430089 | 0.0609 | 0.0624 | 0.0103 | 1.231216 | 1.229722 |
| 2459820 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.079635 | 0.077319 | -0.899308 | -0.884137 | 0.269485 | 0.075550 | 0.676304 | -0.025789 | 0.0818 | 0.0860 | 0.0165 | 1.270711 | 1.270520 |
| 2459817 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.695376 | -0.020239 | -0.895388 | -0.940352 | -1.373590 | -1.465802 | -0.162888 | -0.512453 | 0.1021 | 0.0991 | 0.0265 | 1.235580 | 1.229867 |
| 2459816 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.884608 | -0.072239 | -1.187488 | -0.768549 | -0.450321 | -1.421174 | 2.556973 | 0.268362 | 0.8398 | 0.5705 | 0.6082 | 5.033270 | 3.898425 |
| 2459815 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.774894 | -0.157807 | -1.127003 | -1.055108 | -1.142892 | -1.524181 | 2.097582 | 0.299673 | 0.7893 | 0.6376 | 0.5335 | 4.830366 | 3.942608 |
| 2459814 | digital_maintenance | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Power | 28.351495 | 0.088031 | 16.608631 | 5.802777 | 28.351495 | 2.426509 | 20.034128 | 7.883218 | 16.376252 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Temporal Variability | 43.619041 | -1.015484 | 21.081065 | 6.623990 | 30.273206 | 2.374841 | 43.619041 | 1.951506 | 19.248025 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Power | 25.193428 | -1.525529 | 18.698659 | 5.587029 | 25.193428 | 0.514666 | 19.971260 | 0.720318 | 14.354501 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Variability | 0.326129 | 0.046593 | -0.872784 | -0.367069 | -0.653302 | 0.040656 | 0.326129 | -0.144853 | 0.214865 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 0.691008 | -0.109145 | -1.199933 | -0.091815 | -0.494378 | 0.124797 | 0.121114 | -0.023156 | 0.691008 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 2.057112 | -0.120796 | -0.625575 | -0.919268 | -0.039788 | -0.274348 | 0.293951 | 0.329851 | 2.057112 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Power | 0.756137 | 0.089286 | -0.589371 | -0.021252 | 0.756137 | 0.015244 | -0.550210 | 0.755617 | 0.239145 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Power | 2.909079 | 0.571934 | -0.970414 | 2.909079 | -0.479900 | 2.658965 | -0.347972 | 0.395658 | 0.672608 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Power | 2.192107 | -0.842866 | -0.116295 | -0.245523 | 2.192107 | 0.291778 | 0.174430 | 0.559489 | 0.550305 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Shape | 3.463715 | 1.316608 | 3.463715 | -0.780282 | 2.268893 | -0.128023 | -1.312248 | 1.057442 | 0.131130 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Temporal Variability | 10095.822977 | 123.370365 | 178.189959 | 62.792671 | 95.196935 | 1714.561851 | 10095.822977 | 1903.011361 | 5348.794008 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Temporal Discontinuties | 8038.498113 | 57.751576 | 33.252780 | 338.238260 | 171.482508 | 1149.695646 | 293.418959 | 8038.498113 | 2200.176445 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Temporal Variability | 1.353649 | 0.126824 | -1.227682 | -0.763148 | -1.040233 | 1.353649 | 0.550772 | -0.395604 | -0.012972 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 0.609285 | -0.239844 | -1.001498 | -1.012562 | -0.456043 | -1.048671 | -0.634340 | -0.470720 | 0.609285 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Temporal Variability | 2.413572 | -1.045131 | -0.671448 | 0.675843 | 0.512969 | 2.413572 | 0.653487 | 0.177135 | 2.033700 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 0.689732 | -1.274468 | -0.166524 | -1.091725 | -0.631776 | 0.161877 | -0.126737 | 0.689732 | -0.279811 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 1.727950 | -1.098854 | -1.214417 | -0.277623 | 0.477991 | 0.004178 | 1.386554 | 1.727950 | -0.235108 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 1.402941 | 0.314623 | -0.010609 | -1.091157 | -0.979768 | -0.504443 | 0.360673 | 1.402941 | 0.059715 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 0.947434 | 0.697708 | -1.327892 | -0.901711 | -0.933558 | 0.408488 | 0.024275 | -0.282335 | 0.947434 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 3.243314 | 0.570654 | -0.691716 | -0.751137 | -0.697732 | -0.648938 | 0.131768 | 1.942140 | 3.243314 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Temporal Discontinuties | 1.459807 | -0.589469 | 0.156941 | -0.833998 | -0.746646 | -0.339981 | -0.099854 | 1.398413 | 1.459807 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 0.582925 | -0.022717 | 0.187826 | -0.833903 | -0.805722 | -0.649264 | -0.012712 | -0.369614 | 0.582925 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 0.729328 | 0.210012 | -1.109242 | -0.702254 | -1.023716 | -0.524133 | -0.645211 | 0.109290 | 0.729328 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 1.455356 | -0.438987 | -0.474249 | -1.029780 | -0.902210 | 0.140384 | -0.085869 | 1.455356 | -0.117540 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Shape | 0.136650 | 0.136650 | -0.505161 | -0.795755 | -0.819738 | -1.314302 | -0.856199 | -0.254507 | -0.027468 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Temporal Discontinuties | 4.535869 | -0.096550 | 0.439456 | -0.934749 | -0.891535 | -0.358732 | -0.946555 | 2.799916 | 4.535869 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 0.619999 | 0.278335 | -1.477998 | -0.919261 | -1.079256 | -0.786278 | -0.512177 | 0.430089 | 0.619999 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 0.676304 | -1.079635 | 0.077319 | -0.899308 | -0.884137 | 0.269485 | 0.075550 | 0.676304 | -0.025789 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Shape | -0.020239 | -0.695376 | -0.020239 | -0.895388 | -0.940352 | -1.373590 | -1.465802 | -0.162888 | -0.512453 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 2.556973 | -0.072239 | -0.884608 | -0.768549 | -1.187488 | -1.421174 | -0.450321 | 0.268362 | 2.556973 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | ee Temporal Discontinuties | 2.097582 | -0.157807 | -0.774894 | -1.055108 | -1.127003 | -1.524181 | -1.142892 | 0.299673 | 2.097582 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135 | N12 | digital_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |